IEEE Trans Neural Netw Learn Syst. 2016 Nov;27(11):2426-2439. doi: 10.1109/TNNLS.2015.2487364. Epub 2015 Oct 28.
Real-world data are often acquired as a collection of matrices rather than as a single matrix. Such multiblock data are naturally linked and typically share some common features while at the same time exhibiting their own individual features, reflecting the underlying data generation mechanisms. To exploit the linked nature of data, we propose a new framework for common and individual feature extraction (CIFE) which identifies and separates the common and individual features from the multiblock data. Two efficient algorithms termed common orthogonal basis extraction (COBE) are proposed to extract common basis is shared by all data, independent on whether the number of common components is known beforehand. Feature extraction is then performed on the common and individual subspaces separately, by incorporating dimensionality reduction and blind source separation techniques. Comprehensive experimental results on both the synthetic and real-world data demonstrate significant advantages of the proposed CIFE method in comparison with the state-of-the-art.
真实世界的数据通常是作为矩阵的集合而不是单个矩阵获得的。这种多块数据是自然相关的，通常具有一些共同的特征，同时也表现出自己的个体特征，反映了潜在的数据生成机制。为了利用数据的链接性质，我们提出了一种新的共同和个体特征提取（CIFE）框架，该框架从多块数据中识别和分离共同特征和个体特征。提出了两种有效的算法，称为共同正交基提取（COBE），用于提取所有数据共享的共同基础，而不考虑是否事先知道共同分量的数量。然后，通过结合降维和盲源分离技术，分别在共同和个体子空间上进行特征提取。在合成和真实世界数据上的综合实验结果表明，与最先进的方法相比，所提出的 CIFE 方法具有显著的优势。
